{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visual Question Answering in gluon"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is a notebook for implementing visual question answering in gluon."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from __future__ import print_function\n",
    "import numpy as np\n",
    "import mxnet as mx\n",
    "import mxnet.ndarray as F\n",
    "import mxnet.contrib.ndarray as C\n",
    "import mxnet.gluon as gluon\n",
    "from mxnet.gluon import nn\n",
    "from mxnet import autograd\n",
    "import bisect\n",
    "from IPython.core.display import display, HTML\n",
    "import logging\n",
    "logging.basicConfig(level=logging.INFO)\n",
    "import os\n",
    "from mxnet.test_utils import download\n",
    "import json\n",
    "from IPython.display import HTML, display"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The VQA dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the VQA dataset, for each sample, there is one image and one question. The label is the answer for the question regarding the image. You can download the VQA1.0 dataset from <a href=\"http://www.visualqa.org/vqa_v1_download.html\">VQA</a> website. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "![](../img/pizza.png )\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You need to preprocess the data:\n",
    "\n",
    "(1) Extract the samples from original json files. \n",
    "\n",
    "(2) Filter the samples giving top k answers(k can be 1000, 2000...). This will make the prediction easier."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pretrained Models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Usually people use pretrained models to extract features from the image and question.\n",
    "\n",
    "__Image pretrained model__: \n",
    "\n",
    "<a href=\"https://arxiv.org/abs/1409.1556\">VGG</a>: A key aspect of VGG was to use many convolutional blocks with relatively narrow kernels, followed by a max-pooling step and to repeat this block multiple times. \n",
    "\n",
    "<a href=\"https://arxiv.org/abs/1512.03385\">Resnet</a>: It is a residual learning framework to ease the training of networks that are substantially deep. It reformulate the layers as learning residual functions with reference to the layer inputs, instead of learning unreferenced functions.\n",
    "\n",
    "__Question pretrained model__: \n",
    "\n",
    "<a href=\"https://code.google.com/archive/p/word2vec/\">Word2Vec</a>: The word2vec tool takes a text corpus as input and produces the word vectors as output. It first constructs a vocabulary from the training text data and then learns vector representation of words. The model contains 300-dimensional vectors for 3 million words and phrases. \n",
    "\n",
    "<a href=\"https://nlp.stanford.edu/projects/glove/\">Glove</a>: Similar to Word2Vec, it is a word embedding dataset. It contains 100/200/300-dimensional vectors for 2 million words.\n",
    "\n",
    "<a href=\"https://arxiv.org/abs/1506.06726\">skipthought</a>: This is an encoder-decoder model that tries to reconstruct the surrounding sentences of an encoded passage. Sentences that share semantic and syntactic properties are thus mapped to similar vector representations. Different from the previous two model, this is a sentence based model.\n",
    "\n",
    "<a href=\"https://research.google.com/pubs/pub45610.html\">GNMT encoder</a>: We propose using the encoder of google neural machine translation system to extract the question features. \n",
    "\n",
    "__We will discuss about how to extract the features <a href=\"https://github.com/shiyangdaisy23/vqa-mxnet-gluon/blob/master/extract-feature.ipynb\">here</a> in details.__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Define the model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We define out model with gluon. gluon.Block is the basic building block of models. If any operator is not defined under gluon, you can use mxnet.ndarray operators to subsititude. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Some parameters we are going to use\n",
    "batch_size = 64\n",
    "ctx = mx.cpu()\n",
    "compute_size  = batch_size\n",
    "out_dim = 10000\n",
    "gpus = 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the __first model__, we will concatenate the image and question features and use multilayer perception(MLP) to predict the answer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class Net1(gluon.Block):\n",
    "    def __init__(self, **kwargs):\n",
    "        super(Net1, self).__init__(**kwargs)\n",
    "        with self.name_scope():\n",
    "            # layers created in name_scope will inherit name space\n",
    "            # from parent layer.\n",
    "            self.bn = nn.BatchNorm()\n",
    "            self.dropout = nn.Dropout(0.3)\n",
    "            self.fc1 = nn.Dense(8192,activation=\"relu\")\n",
    "            self.fc2 = nn.Dense(1000)\n",
    "            \n",
    "\n",
    "    def forward(self, x):\n",
    "        x1 = F.L2Normalization(x[0])\n",
    "        x2 = F.L2Normalization(x[1])\n",
    "        z = F.concat(x1,x2,dim=1)\n",
    "        z = self.fc1(z)\n",
    "        z = self.bn(z)\n",
    "        z = self.dropout(z)\n",
    "        z = self.fc2(z)\n",
    "        return z\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the __second model__, instead of linearly combine the image and text features, we use count sketch to estimate the outer product of the image and question features. It is also named as multimodel compact bilinear pooling(MCB).\n",
    "\n",
    "This method was proposed in <a href=\"https://arxiv.org/abs/1606.01847\">Multimodal Compact Bilinear Pooling for VQA</a>. The key idea is:\n",
    "\n",
    "$\\quad \\quad \\quad \\quad \\quad \\quad \\quad \\quad \\quad \\quad \\quad \\quad \\quad \\quad \\psi(x \\otimes y, h,s) = \\psi(x,h,s) \\star \\psi(y,h,s)$\n",
    "\n",
    "where $\\psi$ is the count sketch operator, $x,y$ are the inputs, $h, s$ are the hash tables, $\\otimes$ defines outer product and $\\star$ is the convolution operator. This can further be simplified by using FFT properties: convolution in time domain equals to elementwise product in frequency domain.\n",
    "\n",
    "One improvement we made is adding ones vectors to each features before count sketch. The intuition is: given input vectors $x,y$, estimating outer product between $[x,1s]$ and $[y, 1s]$ gives us information more than just $x \\otimes y$. It also contains information of $x$ and $y$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Net2(gluon.Block):\n",
    "    def __init__(self, **kwargs):\n",
    "        super(Net2, self).__init__(**kwargs)\n",
    "        with self.name_scope():\n",
    "            # layers created in name_scope will inherit name space\n",
    "            # from parent layer.\n",
    "            self.bn = nn.BatchNorm()\n",
    "            self.dropout = nn.Dropout(0.3)\n",
    "            self.fc1 = nn.Dense(8192,activation=\"relu\")\n",
    "            self.fc2 = nn.Dense(1000)\n",
    "            \n",
    "\n",
    "    def forward(self, x):\n",
    "        x1 = F.L2Normalization(x[0])\n",
    "        x2 = F.L2Normalization(x[1])\n",
    "        text_ones = F.ones((batch_size/gpus, 2048),ctx = ctx)\n",
    "        img_ones = F.ones((batch_size/gpus, 1024),ctx = ctx)\n",
    "        text_data = F.Concat(x1, text_ones,dim = 1)\n",
    "        image_data = F.Concat(x2,img_ones,dim = 1)\n",
    "        # Initialize hash tables\n",
    "        S1 = F.array(np.random.randint(0, 2, (1,3072))*2-1,ctx = ctx)\n",
    "        H1 = F.array(np.random.randint(0, out_dim,(1,3072)),ctx = ctx)\n",
    "        S2 = F.array(np.random.randint(0, 2, (1,3072))*2-1,ctx = ctx)\n",
    "        H2 = F.array(np.random.randint(0, out_dim,(1,3072)),ctx = ctx)\n",
    "        # Count sketch\n",
    "        cs1 = C.count_sketch( data = image_data, s=S1, h = H1 ,name='cs1',out_dim = out_dim) \n",
    "        cs2 = C.count_sketch( data = text_data, s=S2, h = H2 ,name='cs2',out_dim = out_dim) \n",
    "        fft1 = C.fft(data = cs1, name='fft1', compute_size = compute_size) \n",
    "        fft2 = C.fft(data = cs2, name='fft2', compute_size = compute_size) \n",
    "        c = fft1 * fft2\n",
    "        ifft1 = C.ifft(data = c, name='ifft1', compute_size = compute_size) \n",
    "        # MLP\n",
    "        z = self.fc1(ifft1)\n",
    "        z = self.bn(z)\n",
    "        z = self.dropout(z)\n",
    "        z = self.fc2(z)\n",
    "        return z"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__We will introduce attention model in this <a href=\"https://github.com/shiyangdaisy23/vqa-mxnet-gluon/blob/master/Attention-VQA-gluon.ipynb\">notebook</a>.__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Iterator"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The inputs of the data iterator are extracted image and question features. At each step, the data iterator will return a data batch list: question data batch and image data batch. \n",
    "\n",
    "We need to seperate the data batches by the length of the input data because the input questions are in different lengths. The $buckets$ parameter defines the max length you want to keep in the data iterator. Here since we already used pretrained model to extract the question feature, the question length is fixed as the output of the pretrained model.\n",
    "\n",
    "The $layout$ parameter defines the layout of the data iterator output. \"N\" specify where is the data batch dimension is.\n",
    "\n",
    "$reset()$ function is called after every epoch. $next()$ function is call after each batch."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "class VQAtrainIter(mx.io.DataIter):\n",
    "    def __init__(self, img, sentences, answer, batch_size, buckets=None, invalid_label=-1,\n",
    "                 text_name='text', img_name = 'image', label_name='softmax_label', dtype='float32', layout='NTC'):\n",
    "        super(VQAtrainIter, self).__init__()\n",
    "        if not buckets:\n",
    "            buckets = [i for i, j in enumerate(np.bincount([len(s) for s in sentences]))\n",
    "                       if j >= batch_size]\n",
    "        buckets.sort()\n",
    "\n",
    "        ndiscard = 0\n",
    "        self.data = [[] for _ in buckets]\n",
    "        for i in range(len(sentences)):\n",
    "            buck = bisect.bisect_left(buckets, len(sentences[i]))\n",
    "            if buck == len(buckets):\n",
    "                ndiscard += 1\n",
    "                continue\n",
    "            buff = np.full((buckets[buck],), invalid_label, dtype=dtype)\n",
    "            buff[:len(sentences[i])] = sentences[i]\n",
    "            self.data[buck].append(buff)\n",
    "\n",
    "        self.data = [np.asarray(i, dtype=dtype) for i in self.data]\n",
    "        self.answer = answer\n",
    "        self.img = img\n",
    "        print(\"WARNING: discarded %d sentences longer than the largest bucket.\"%ndiscard)\n",
    "\n",
    "        self.batch_size = batch_size\n",
    "        self.buckets = buckets\n",
    "        self.text_name = text_name\n",
    "        self.img_name = img_name\n",
    "        self.label_name = label_name\n",
    "        self.dtype = dtype\n",
    "        self.invalid_label = invalid_label\n",
    "        self.nd_text = []\n",
    "        self.nd_img = []\n",
    "        self.ndlabel = []\n",
    "        self.major_axis = layout.find('N')\n",
    "        self.default_bucket_key = max(buckets)\n",
    "\n",
    "        if self.major_axis == 0:\n",
    "            self.provide_data = [(text_name, (batch_size, self.default_bucket_key)),\n",
    "                                 (img_name, (batch_size, self.default_bucket_key))]\n",
    "            self.provide_label = [(label_name, (batch_size, self.default_bucket_key))]\n",
    "        elif self.major_axis == 1:\n",
    "            self.provide_data = [(text_name, (self.default_bucket_key, batch_size)),\n",
    "                                 (img_name, (self.default_bucket_key, batch_size))]\n",
    "            self.provide_label = [(label_name, (self.default_bucket_key, batch_size))]\n",
    "        else:\n",
    "            raise ValueError(\"Invalid layout %s: Must by NT (batch major) or TN (time major)\")\n",
    "\n",
    "        self.idx = []\n",
    "        for i, buck in enumerate(self.data):\n",
    "            self.idx.extend([(i, j) for j in range(0, len(buck) - batch_size + 1, batch_size)])\n",
    "        self.curr_idx = 0\n",
    "\n",
    "        self.reset()\n",
    "\n",
    "    def reset(self):\n",
    "        self.curr_idx = 0\n",
    "        self.nd_text = []\n",
    "        self.nd_img = []\n",
    "        self.ndlabel = []\n",
    "        for buck in self.data:\n",
    "            label = np.empty_like(buck.shape[0])\n",
    "            label = self.answer\n",
    "            self.nd_text.append(mx.ndarray.array(buck, dtype=self.dtype))\n",
    "            self.nd_img.append(mx.ndarray.array(self.img, dtype=self.dtype))\n",
    "            self.ndlabel.append(mx.ndarray.array(label, dtype=self.dtype))\n",
    "\n",
    "    def next(self):\n",
    "        if self.curr_idx == len(self.idx):\n",
    "            raise StopIteration\n",
    "        i, j = self.idx[self.curr_idx]\n",
    "        self.curr_idx += 1\n",
    "\n",
    "        if self.major_axis == 1:\n",
    "            img = self.nd_img[i][j:j + self.batch_size].T\n",
    "            text = self.nd_text[i][j:j + self.batch_size].T\n",
    "            label = self.ndlabel[i][j:j+self.batch_size]\n",
    "        else:\n",
    "            img = self.nd_img[i][j:j + self.batch_size]\n",
    "            text = self.nd_text[i][j:j + self.batch_size]\n",
    "            label = self.ndlabel[i][j:j+self.batch_size]\n",
    "        \n",
    "        data = [text, img]\n",
    "        return mx.io.DataBatch(data, [label],\n",
    "                         bucket_key=self.buckets[i],\n",
    "                         provide_data=[(self.text_name, text.shape),(self.img_name, img.shape)],\n",
    "                         provide_label=[(self.label_name, label.shape)])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we will use subset of VQA dataset in this tutorial. We extract the image feature from ResNet-152, text feature from GNMT encoder. In first two model, we have 21537 training samples and 1044 validation samples in this tutorial. Image feature is a 2048-dim vector. Question feature is a 1048-dim vector. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:root:Downloading training dataset.\n",
      "INFO:root:downloaded https://apache-mxnet.s3-accelerate.amazonaws.com/gluon/dataset/VQA-notebook/train_question.npz into train_question.npz successfully\n",
      "INFO:root:downloaded https://apache-mxnet.s3-accelerate.amazonaws.com/gluon/dataset/VQA-notebook/train_img.npz into train_img.npz successfully\n",
      "INFO:root:downloaded https://apache-mxnet.s3-accelerate.amazonaws.com/gluon/dataset/VQA-notebook/train_ans.npz into train_ans.npz successfully\n",
      "INFO:root:Downloading validation dataset.\n",
      "INFO:root:downloaded https://apache-mxnet.s3-accelerate.amazonaws.com/gluon/dataset/VQA-notebook/val_question.npz into val_question.npz successfully\n",
      "INFO:root:downloaded https://apache-mxnet.s3-accelerate.amazonaws.com/gluon/dataset/VQA-notebook/val_img.npz into val_img.npz successfully\n",
      "INFO:root:downloaded https://apache-mxnet.s3-accelerate.amazonaws.com/gluon/dataset/VQA-notebook/val_ans.npz into val_ans.npz successfully\n"
     ]
    }
   ],
   "source": [
    "# Download the dataset\n",
    "dataset_files = {'train': ('train_question.npz','train_img.npz','train_ans.npz'),\n",
    "                 'validation': ('val_question.npz','val_img.npz','val_ans.npz'),\n",
    "                 'test':('test_question_id.npz','test_question.npz','test_img_id.npz','test_img.npz','atoi.json','test_question_txt.json')}\n",
    "\n",
    "train_q, train_i, train_a = dataset_files['train']\n",
    "val_q, val_i, val_a = dataset_files['validation']\n",
    "\n",
    "url_format = 'https://apache-mxnet.s3-accelerate.amazonaws.com/gluon/dataset/VQA-notebook/{}'\n",
    "if not os.path.exists(train_q):\n",
    "    logging.info('Downloading training dataset.')\n",
    "    download(url_format.format(train_q),overwrite=True)\n",
    "    download(url_format.format(train_i),overwrite=True)\n",
    "    download(url_format.format(train_a),overwrite=True)\n",
    "if not os.path.exists(val_q):\n",
    "    logging.info('Downloading validation dataset.')\n",
    "    download(url_format.format(val_q),overwrite=True)\n",
    "    download(url_format.format(val_i),overwrite=True)\n",
    "    download(url_format.format(val_a),overwrite=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total training sample: 21537\n",
      "Total validation sample: 1044\n",
      "WARNING: discarded 0 sentences longer than the largest bucket.\n",
      "WARNING: discarded 0 sentences longer than the largest bucket.\n"
     ]
    }
   ],
   "source": [
    "layout = 'NT'\n",
    "bucket = [1024]\n",
    "\n",
    "train_question = np.load(train_q)['x']\n",
    "val_question = np.load(val_q)['x']\n",
    "train_ans = np.load(train_a)['x']\n",
    "val_ans = np.load(val_a)['x']\n",
    "train_img = np.load(train_i)['x']\n",
    "val_img = np.load(val_i)['x']\n",
    "\n",
    "print(\"Total training sample:\",train_ans.shape[0])   \n",
    "print(\"Total validation sample:\",val_ans.shape[0])   \n",
    "\n",
    "data_train  = VQAtrainIter(train_img, train_question, train_ans, batch_size, buckets = bucket,layout=layout)\n",
    "data_eva = VQAtrainIter(val_img, val_question, val_ans, batch_size, buckets = bucket,layout=layout) \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Initialize the Parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "net = Net1()\n",
    "#net = Net2()\n",
    "net.collect_params().initialize(mx.init.Xavier(), ctx=ctx)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loss and Evaluation Metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "loss = gluon.loss.SoftmaxCrossEntropyLoss()\n",
    "\n",
    "metric = mx.metric.Accuracy()\n",
    "\n",
    "def evaluate_accuracy(data_iterator, net):\n",
    "    numerator = 0.\n",
    "    denominator = 0.\n",
    "    \n",
    "    data_iterator.reset()\n",
    "    for i, batch in enumerate(data_iterator):\n",
    "        with autograd.record():\n",
    "            data1 = batch.data[0].as_in_context(ctx)\n",
    "            data2 = batch.data[1].as_in_context(ctx)\n",
    "            data = [data1,data2]\n",
    "            label = batch.label[0].as_in_context(ctx)\n",
    "            output = net(data)\n",
    "        \n",
    "        metric.update([label], [output])\n",
    "    return metric.get()[1]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Optimizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "trainer = gluon.Trainer(net.collect_params(), 'sgd', {'learning_rate': 0.01})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training loop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:root:Best validation acc found. Checkpointing...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 0. Loss: 3.07848375872, Train_acc 0.439319957386, Eval_acc 0.3525390625\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:root:Best validation acc found. Checkpointing...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1. Loss: 2.08781239439, Train_acc 0.478870738636, Eval_acc 0.436820652174\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:root:Best validation acc found. Checkpointing...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 2. Loss: 1.63500481371, Train_acc 0.515536221591, Eval_acc 0.476584201389\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:root:Best validation acc found. Checkpointing...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 3. Loss: 1.45585072303, Train_acc 0.549283114347, Eval_acc 0.513701026119\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:root:Best validation acc found. Checkpointing...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 4. Loss: 1.17097555747, Train_acc 0.579172585227, Eval_acc 0.547500438904\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:root:Best validation acc found. Checkpointing...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 5. Loss: 1.0625076159, Train_acc 0.606460108902, Eval_acc 0.577517947635\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:root:Best validation acc found. Checkpointing...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 6. Loss: 0.832051645247, Train_acc 0.629863788555, Eval_acc 0.60488868656\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:root:Best validation acc found. Checkpointing...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 7. Loss: 0.749606922723, Train_acc 0.650507146662, Eval_acc 0.62833921371\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:root:Best validation acc found. Checkpointing...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 8. Loss: 0.680526961879, Train_acc 0.668269610164, Eval_acc 0.649105093573\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:root:Best validation acc found. Checkpointing...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 9. Loss: 0.53362678042, Train_acc 0.683984375, Eval_acc 0.666923484611\n"
     ]
    }
   ],
   "source": [
    "epochs = 10\n",
    "moving_loss = 0.\n",
    "best_eva = 0\n",
    "for e in range(epochs):\n",
    "    data_train.reset()\n",
    "    for i, batch in enumerate(data_train):\n",
    "        data1 = batch.data[0].as_in_context(ctx)\n",
    "        data2 = batch.data[1].as_in_context(ctx)\n",
    "        data = [data1,data2]\n",
    "        label = batch.label[0].as_in_context(ctx)\n",
    "        with autograd.record():\n",
    "            output = net(data)\n",
    "            cross_entropy = loss(output, label)\n",
    "            cross_entropy.backward()\n",
    "        trainer.step(data[0].shape[0])\n",
    "        \n",
    "        ##########################\n",
    "        #  Keep a moving average of the losses\n",
    "        ##########################\n",
    "        if i == 0:\n",
    "            moving_loss = np.mean(cross_entropy.asnumpy()[0])\n",
    "        else:\n",
    "            moving_loss = .99 * moving_loss + .01 * np.mean(cross_entropy.asnumpy()[0])\n",
    "        #if i % 200 == 0:\n",
    "        #    print(\"Epoch %s, batch %s. Moving avg of loss: %s\" % (e, i, moving_loss))   \n",
    "    eva_accuracy = evaluate_accuracy(data_eva, net)\n",
    "    train_accuracy = evaluate_accuracy(data_train, net)\n",
    "    print(\"Epoch %s. Loss: %s, Train_acc %s, Eval_acc %s\" % (e, moving_loss, train_accuracy, eva_accuracy))\n",
    "    if eva_accuracy > best_eva:\n",
    "            best_eva = eva_accuracy\n",
    "            logging.info('Best validation acc found. Checkpointing...')\n",
    "            net.save_parameters('vqa-mlp-%d.params'%(e))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Try it out!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Currently we have test data for the first two models we mentioned above. After the training loop over Net1 or Net2, we can try it on test data. Here we have 10 test samples. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:root:Downloading test dataset.\n",
      "INFO:root:downloaded https://apache-mxnet.s3-accelerate.amazonaws.com/gluon/dataset/VQA-notebook/test_question_id.npz into test_question_id.npz successfully\n",
      "INFO:root:downloaded https://apache-mxnet.s3-accelerate.amazonaws.com/gluon/dataset/VQA-notebook/test_question.npz into test_question.npz successfully\n",
      "INFO:root:downloaded https://apache-mxnet.s3-accelerate.amazonaws.com/gluon/dataset/VQA-notebook/test_img_id.npz into test_img_id.npz successfully\n",
      "INFO:root:downloaded https://apache-mxnet.s3-accelerate.amazonaws.com/gluon/dataset/VQA-notebook/test_img.npz into test_img.npz successfully\n",
      "INFO:root:downloaded https://apache-mxnet.s3-accelerate.amazonaws.com/gluon/dataset/VQA-notebook/atoi.json into atoi.json successfully\n",
      "INFO:root:downloaded https://apache-mxnet.s3-accelerate.amazonaws.com/gluon/dataset/VQA-notebook/test_question_txt.json into test_question_txt.json successfully\n"
     ]
    }
   ],
   "source": [
    "test = True\n",
    "if test:\n",
    "    test_q_id, test_q, test_i_id, test_i, atoi,text = dataset_files['test']\n",
    "\n",
    "if test and not os.path.exists(test_q):     \n",
    "    logging.info('Downloading test dataset.')\n",
    "    download(url_format.format(test_q_id),overwrite=True)\n",
    "    download(url_format.format(test_q),overwrite=True)\n",
    "    download(url_format.format(test_i_id),overwrite=True)\n",
    "    download(url_format.format(test_i),overwrite=True)\n",
    "    download(url_format.format(atoi),overwrite=True)\n",
    "    download(url_format.format(text),overwrite=True)\n",
    "\n",
    "if test:\n",
    "    test_question = np.load(\"test_question.npz\")['x']\n",
    "    test_img = np.load(\"test_img.npz\")['x']\n",
    "    test_question_id = np.load(\"test_question_id.npz\")['x']\n",
    "    test_img_id = np.load(\"test_img_id.npz\")['x']\n",
    "    #atoi = np.load(\"atoi.json\")['x']    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We pass the test data iterator to the trained model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING: discarded 0 sentences longer than the largest bucket.\n"
     ]
    }
   ],
   "source": [
    "data_test = VQAtrainIter(test_img, test_question, np.zeros((test_img.shape[0],1)), 10, buckets = bucket,layout=layout)\n",
    "for i, batch in enumerate(data_test):\n",
    "    with autograd.record():\n",
    "        data1 = batch.data[0].as_in_context(ctx)\n",
    "        data2 = batch.data[1].as_in_context(ctx)\n",
    "        data = [data1,data2]\n",
    "        #label = batch.label[0].as_in_context(ctx)\n",
    "        #label_one_hot = nd.one_hot(label, 10)\n",
    "        output = net(data)\n",
    "output = np.argmax(output.asnumpy(), axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6\n",
      "Question: Is there a boat in the water?\n"
     ]
    }
   ],
   "source": [
    "idx = np.random.randint(10)\n",
    "print(idx)\n",
    "question = json.load(open(text))\n",
    "print(\"Question:\", question[idx])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:root:Downloading training dataset.\n",
      "INFO:root:downloaded https://apache-mxnet.s3-accelerate.amazonaws.com/gluon/dataset/VQA-notebook/test_images/COCO_test2015_000000419358.jpg into COCO_test2015_000000419358.jpg successfully\n"
     ]
    },
    {
     "data": {
      "image/jpeg": "/9j/4AAQSkZJRgABAQEASABIAAD/4gJASUNDX1BST0ZJTEUAAQEAAAIwQURCRQIQAABtbnRyUkdC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\ndrXLs51hgiekQPQCNFmqOpudmAX/2Q==\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "image_name = 'COCO_test2015_' + str(int(test_img_id[idx])).zfill(12)+'.jpg'\n",
    "if not os.path.exists(image_name):\n",
    "    logging.info('Downloading training dataset.')\n",
    "    download(url_format.format('test_images/'+image_name),overwrite=True)\n",
    "\n",
    "from IPython.display import Image\n",
    "Image(filename=image_name) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Answer: yes\n"
     ]
    }
   ],
   "source": [
    "dataset = json.load(open('atoi.json'))\n",
    "ans = dataset['ix_to_ans'][str(output[idx]+1)]\n",
    "print(\"Answer:\", ans)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For whinges or inquiries, [open an issue on  GitHub.](https://github.com/zackchase/mxnet-the-straight-dope)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.4.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
